The number of available content assets, such as digital, audiovisual assets that can be consumed at any point in time is exploding. As a result, instant, interest driven access to that expanding content universe is becoming more and more important. Digitalization of audiovisual materials together with the broad availability of high speed data transfer has resulted in an ever increasing amount of content that any consumer has available for consumption at any point in time. Examples of large repositories of audiovisual assets on the internet are YouTube, Netflix, and Hulu. They are competing with digital, live TV channels and VoD libraries, offered by TV service providers, e.g. Comcast.
As the consumer's time to select and enjoy the available content assets doesn't increase the importance of conveniently controllable guidance towards the most interesting and appropriate audiovisual content gains in importance.
Typically internet sites, service providers, and device manufacturers offer solutions that allow users to search and navigate their content offerings. The first of these is a simple input means for search using text entry, for example, the search bar from Google or YouTube. Such searching is essentially easy to use (at least on a PC), but returns a lot of results that are irrelevant. Attempting to improve the detail of the search request instantly requires the user to go back one step in the search process which is cumbersome and may take many attempts to obtain good results, which is time consuming. Some sites (e.g. specifications like “site: www.philips.com” or “Include entire text”), require expert skills, which makes the solution unsuitable to the general public.
Another known technique is implicit input means, e.g. tracking what the user is browsing via cookies, making use of his purchase history etc. This takes significant time, before providing useful recommendations. Furthermore, this technique does not allow the user to specifically express his interests.
A further known technique is the use of comparative input means, for example, rating a movie as “liked” or “disliked”. This is equally effective as a simple input means, allowing the user to refine the result on the go without going back. However, as above it returns, at least initially, many irrelevant results and can be time consuming to refine it to obtain useful results.
Another known technique uses complex input means, for example, allowing the user to explicitly state his preferences like “I love Alfred Hitchcock” but “slightly dislike Horror Movies”. This allows more control of search results and navigation, but is too overwhelming to be useful for the general public, especially as the user first needs to understand which criteria can be used to constrain the result set, and how they need to be applied. Also these techniques are not suited to refine on the go.
An example of such a complex input means is disclosed by U.S. Pat. No. 7,617,511 in which, whilst browsing an electronic programming guide (EPG), the user can enter new preference ratings or modify previously saved ratings for program attributes. This results in recommendations being made based on the user's preferences. However, as mentioned above, the user first needs to understand how the criteria are used and how to apply it. Furthermore, any further browsing of the EPG is limited to these preferences.